Serving a personally relevant digital advertisement to a specific user having specific demographics can be a compute-intensive process, yet users are unforgiving when it comes to latency expectations. Worse, there may be tens or hundreds (or thousands) of ads that are personally relevant to a particular user, and determining which one is most relevant can involve literally millions of evaluations and decisions. For example, perhaps hundreds (or thousands, or more) advertisers seek to display their digital advertisements over the Internet to reach individuals within a target set having very specific demographics (e.g. male, age 40-48, graduate of Stanford, living in California or New York, etc). Each time a web page is requested by a user via the Internet represents an impression opportunity to display an advertisement in some portion of the web page to the individual Internet user, and each impression opportunity can be satisfied by displaying a selected digital advertisement to the individual. The time duration between the individual's landing on a web page and the satisfaction of an impression opportunity is typically much less than one second. Yet, as digital media gains in popularity, the targeting of ever more specific demographics increases, as does the computing requirements to perform the ever more specific targeting so as to select and display a personally relevant digital advertisement to the user.
Certain legacy relevance models have been used, but such legacy models begin to fail when (1) the targeting requirements become more and more specific, and (2) tight real-time latency requirements are present. Some work has been done to exploit the availability of multiple computing platforms (e.g. multiple advertisement servers), however such work has been largely limited to exploiting parallelization at the server level, and has not exploited finer-grained parallelization opportunities presented by multiple core processors.
Thus, for these and other reasons, there exists a need for high performance personalized advertisement serving by exploiting thread assignments in multiple core computing environments.